Overview

Brought to you by YData

Dataset statistics

Number of variables39
Number of observations23064
Missing cells75586
Missing cells (%)8.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.3 MiB
Average record size in memory832.9 B

Variable types

Numeric18
Text7
Categorical12
DateTime2

Alerts

Image has constant value "None" Constant
AppliqueFodec is highly overall correlated with IDAr_Collection and 10 other fieldsHigh correlation
DateValidationNomenclature is highly overall correlated with NomenclatureValideHigh correlation
Etat is highly overall correlated with ModifiePar and 1 other fieldsHigh correlation
IDAr_Collection is highly overall correlated with AppliqueFodec and 10 other fieldsHigh correlation
IDAr_Couleur is highly overall correlated with IDAr_Collection and 4 other fieldsHigh correlation
IDAr_Looks is highly overall correlated with IDSupportArticleHigh correlation
IDArticle is highly overall correlated with AppliqueFodec and 11 other fieldsHigh correlation
IDFournisseur is highly overall correlated with AppliqueFodec and 9 other fieldsHigh correlation
IDPatronnage is highly overall correlated with AppliqueFodec and 9 other fieldsHigh correlation
IDPays is highly overall correlated with AppliqueFodec and 1 other fieldsHigh correlation
IDSaison is highly overall correlated with AppliqueFodec and 5 other fieldsHigh correlation
IDSupportArticle is highly overall correlated with IDAr_LooksHigh correlation
IDUsine is highly overall correlated with AppliqueFodec and 9 other fieldsHigh correlation
ModifiePar is highly overall correlated with AppliqueFodec and 4 other fieldsHigh correlation
NomenclatureValide is highly overall correlated with DateValidationNomenclatureHigh correlation
NumInterne is highly overall correlated with AppliqueFodec and 8 other fieldsHigh correlation
PieceCarton is highly overall correlated with AppliqueFodec and 6 other fieldsHigh correlation
PoidsNet is highly overall correlated with IDAr_Collection and 6 other fieldsHigh correlation
Prix is highly overall correlated with IDArticle and 3 other fieldsHigh correlation
PrixAchat is highly overall correlated with Prix and 1 other fieldsHigh correlation
PrixFac is highly overall correlated with PoidsNet and 2 other fieldsHigh correlation
SaisiPar is highly overall correlated with Etat and 7 other fieldsHigh correlation
TauxTVA is highly overall correlated with AppliqueFodec and 3 other fieldsHigh correlation
TypeArticleService is highly overall correlated with IDAr_Collection and 3 other fieldsHigh correlation
Etat is highly imbalanced (97.4%) Imbalance
TauxTVA is highly imbalanced (61.0%) Imbalance
NomenclatureValide is highly imbalanced (99.8%) Imbalance
TypeArticleService is highly imbalanced (75.3%) Imbalance
DateValidationNomenclature is highly imbalanced (99.9%) Imbalance
Matiere is highly imbalanced (99.1%) Imbalance
SaisiPar has 16242 (70.4%) missing values Missing
ModifiePar has 16670 (72.3%) missing values Missing
ModifieLe has 16293 (70.6%) missing values Missing
Composition has 9997 (43.3%) missing values Missing
Reference has 15891 (68.9%) missing values Missing
Prix is highly skewed (γ1 = 101.1198322) Skewed
PrixFac is highly skewed (γ1 = 101.1198396) Skewed
PoidsBrut is highly skewed (γ1 = 148.3780906) Skewed
PoidsNet is highly skewed (γ1 = 113.5731678) Skewed
PrixAchat is highly skewed (γ1 = 101.1198529) Skewed
IDArticle has unique values Unique
IDAr_Collection has 15978 (69.3%) zeros Zeros
IDAr_Couleur has 7078 (30.7%) zeros Zeros
Prix has 4917 (21.3%) zeros Zeros
PrixFac has 3088 (13.4%) zeros Zeros
PoidsBrut has 22996 (99.7%) zeros Zeros
PoidsNet has 8277 (35.9%) zeros Zeros
IDArSousFamille has 463 (2.0%) zeros Zeros
IDGrille has 429 (1.9%) zeros Zeros
IDSaison has 766 (3.3%) zeros Zeros
NumInterne has 15119 (65.6%) zeros Zeros
IDFournisseur has 15825 (68.6%) zeros Zeros
PrixAchat has 2249 (9.8%) zeros Zeros
IDPays has 430 (1.9%) zeros Zeros
IDUsine has 16498 (71.5%) zeros Zeros
IDSupportArticle has 22140 (96.0%) zeros Zeros
IDAr_Looks has 22291 (96.6%) zeros Zeros

Reproduction

Analysis started2025-04-18 17:22:00.506619
Analysis finished2025-04-18 17:23:07.535293
Duration1 minute and 7.03 seconds
Software versionydata-profiling vv4.13.0
Download configurationconfig.json

Variables

IDAr_Collection
Real number (ℝ)

High correlation  Zeros 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3717482
Minimum0
Maximum13
Zeros15978
Zeros (%)69.3%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-04-18T18:23:07.587962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1876311
Coefficient of variation (CV)1.7656306
Kurtosis0.9126153
Mean2.3717482
Median Absolute Deviation (MAD)0
Skewness1.5642717
Sum54702
Variance17.536254
MonotonicityNot monotonic
2025-04-18T18:23:07.671226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 15978
69.3%
5 1631
 
7.1%
4 1222
 
5.3%
12 1096
 
4.8%
13 878
 
3.8%
11 815
 
3.5%
10 534
 
2.3%
1 470
 
2.0%
2 138
 
0.6%
6 137
 
0.6%
Other values (4) 165
 
0.7%
ValueCountFrequency (%)
0 15978
69.3%
1 470
 
2.0%
2 138
 
0.6%
3 23
 
0.1%
4 1222
 
5.3%
5 1631
 
7.1%
6 137
 
0.6%
7 63
 
0.3%
8 1
 
< 0.1%
9 78
 
0.3%
ValueCountFrequency (%)
13 878
3.8%
12 1096
4.8%
11 815
3.5%
10 534
 
2.3%
9 78
 
0.3%
8 1
 
< 0.1%
7 63
 
0.3%
6 137
 
0.6%
5 1631
7.1%
4 1222
5.3%

IDArticle
Real number (ℝ)

High correlation  Unique 

Distinct23064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11740.141
Minimum2
Maximum23883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-04-18T18:23:07.827581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1189.15
Q15801.75
median11570.5
Q317704.25
95-th percentile22694.85
Maximum23883
Range23881
Interquartile range (IQR)11902.5

Descriptive statistics

Standard deviation6883.3795
Coefficient of variation (CV)0.58631149
Kurtosis-1.1844723
Mean11740.141
Median Absolute Deviation (MAD)5925
Skewness0.056604879
Sum2.7077461 × 108
Variance47380913
MonotonicityStrictly increasing
2025-04-18T18:23:07.989559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23883 1
 
< 0.1%
2 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
23867 1
 
< 0.1%
23866 1
 
< 0.1%
Other values (23054) 23054
> 99.9%
ValueCountFrequency (%)
2 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
ValueCountFrequency (%)
23883 1
< 0.1%
23882 1
< 0.1%
23881 1
< 0.1%
23880 1
< 0.1%
23879 1
< 0.1%
23878 1
< 0.1%
23877 1
< 0.1%
23876 1
< 0.1%
23875 1
< 0.1%
23874 1
< 0.1%

Code
Text

Distinct20296
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-04-18T18:23:08.270424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length19
Mean length7.4997399
Min length2

Characters and Unicode

Total characters172974
Distinct characters64
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18946 ?
Unique (%)82.1%

Sample

1st rowBHNP282
2nd rowAENC88
3rd rowAENU104A
4th rowAENU102AA
5th rowAENU102
ValueCountFrequency (%)
cenc154 45
 
0.2%
c1 32
 
0.1%
t1 29
 
0.1%
r1 27
 
0.1%
cent114 20
 
0.1%
cent094 17
 
0.1%
cenc168 16
 
0.1%
p1 15
 
0.1%
cenc216 14
 
0.1%
xenj413 14
 
0.1%
Other values (20296) 23056
99.0%
2025-04-18T18:23:08.851205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 24631
 
14.2%
A 12746
 
7.4%
1 12611
 
7.3%
E 11895
 
6.9%
H 11600
 
6.7%
2 7693
 
4.4%
0 7417
 
4.3%
4 5981
 
3.5%
X 5973
 
3.5%
3 5819
 
3.4%
Other values (54) 66608
38.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 108470
62.7%
Decimal Number 64033
37.0%
Lowercase Letter 224
 
0.1%
Space Separator 222
 
0.1%
Dash Punctuation 21
 
< 0.1%
Connector Punctuation 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 24631
22.7%
A 12746
11.8%
E 11895
11.0%
H 11600
10.7%
X 5973
 
5.5%
R 5266
 
4.9%
C 5121
 
4.7%
T 4181
 
3.9%
B 3452
 
3.2%
D 3302
 
3.0%
Other values (17) 20303
18.7%
Lowercase Letter
ValueCountFrequency (%)
e 41
18.3%
a 28
12.5%
o 20
8.9%
i 18
 
8.0%
t 17
 
7.6%
l 12
 
5.4%
p 11
 
4.9%
u 11
 
4.9%
n 10
 
4.5%
r 8
 
3.6%
Other values (14) 48
21.4%
Decimal Number
ValueCountFrequency (%)
1 12611
19.7%
2 7693
12.0%
0 7417
11.6%
4 5981
9.3%
3 5819
9.1%
6 5606
8.8%
5 5581
8.7%
7 4580
 
7.2%
8 4439
 
6.9%
9 4306
 
6.7%
Space Separator
ValueCountFrequency (%)
222
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 108694
62.8%
Common 64280
37.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 24631
22.7%
A 12746
11.7%
E 11895
10.9%
H 11600
10.7%
X 5973
 
5.5%
R 5266
 
4.8%
C 5121
 
4.7%
T 4181
 
3.8%
B 3452
 
3.2%
D 3302
 
3.0%
Other values (41) 20527
18.9%
Common
ValueCountFrequency (%)
1 12611
19.6%
2 7693
12.0%
0 7417
11.5%
4 5981
9.3%
3 5819
9.1%
6 5606
8.7%
5 5581
8.7%
7 4580
 
7.1%
8 4439
 
6.9%
9 4306
 
6.7%
Other values (3) 247
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172969
> 99.9%
None 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 24631
 
14.2%
A 12746
 
7.4%
1 12611
 
7.3%
E 11895
 
6.9%
H 11600
 
6.7%
2 7693
 
4.4%
0 7417
 
4.3%
4 5981
 
3.5%
X 5973
 
3.5%
3 5819
 
3.4%
Other values (51) 66603
38.5%
None
ValueCountFrequency (%)
Ä 3
60.0%
è 1
 
20.0%
é 1
 
20.0%
Distinct18641
Distinct (%)81.4%
Missing173
Missing (%)0.8%
Memory size1.5 MiB
2025-04-18T18:23:09.128389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length60
Median length48
Mean length10.500721
Min length2

Characters and Unicode

Total characters240372
Distinct characters82
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16769 ?
Unique (%)73.3%

Sample

1st rowCITY P1 BLACK
2nd rowLOUISON C1 LOUISON NOIR
3rd rowNATALIA ECRU LUREX LIGHT GOLD
4th rowNANNI GRIS CHAINE SILVER LUREX-2002
5th rowNANNI ROUGE POMPIER LUREX-2045
ValueCountFrequency (%)
r1 3007
 
6.3%
c1 2078
 
4.3%
j1 1397
 
2.9%
p1 1290
 
2.7%
ml 1019
 
2.1%
bo 930
 
1.9%
mc 883
 
1.8%
t1 874
 
1.8%
noir 840
 
1.7%
v1 526
 
1.1%
Other values (11319) 35173
73.3%
2025-04-18T18:23:09.524283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25194
 
10.5%
E 21259
 
8.8%
A 20996
 
8.7%
R 16210
 
6.7%
I 15446
 
6.4%
L 14262
 
5.9%
O 13824
 
5.8%
N 11904
 
5.0%
1 11110
 
4.6%
M 9908
 
4.1%
Other values (72) 80259
33.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 199502
83.0%
Space Separator 25194
 
10.5%
Decimal Number 13020
 
5.4%
Lowercase Letter 1295
 
0.5%
Dash Punctuation 863
 
0.4%
Other Punctuation 469
 
0.2%
Close Punctuation 12
 
< 0.1%
Open Punctuation 12
 
< 0.1%
Math Symbol 4
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 21259
 
10.7%
A 20996
 
10.5%
R 16210
 
8.1%
I 15446
 
7.7%
L 14262
 
7.1%
O 13824
 
6.9%
N 11904
 
6.0%
M 9908
 
5.0%
C 9893
 
5.0%
S 9394
 
4.7%
Other values (20) 56406
28.3%
Lowercase Letter
ValueCountFrequency (%)
e 189
14.6%
a 128
 
9.9%
n 101
 
7.8%
o 91
 
7.0%
r 86
 
6.6%
t 85
 
6.6%
m 83
 
6.4%
l 71
 
5.5%
i 71
 
5.5%
c 67
 
5.2%
Other values (17) 323
24.9%
Decimal Number
ValueCountFrequency (%)
1 11110
85.3%
2 1117
 
8.6%
3 256
 
2.0%
0 154
 
1.2%
4 119
 
0.9%
5 61
 
0.5%
7 60
 
0.5%
8 55
 
0.4%
6 48
 
0.4%
9 40
 
0.3%
Other Punctuation
ValueCountFrequency (%)
/ 391
83.4%
' 26
 
5.5%
. 21
 
4.5%
, 12
 
2.6%
% 11
 
2.3%
& 7
 
1.5%
? 1
 
0.2%
Close Punctuation
ValueCountFrequency (%)
] 7
58.3%
) 5
41.7%
Open Punctuation
ValueCountFrequency (%)
[ 7
58.3%
( 5
41.7%
Space Separator
ValueCountFrequency (%)
25194
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 863
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 200797
83.5%
Common 39575
 
16.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 21259
 
10.6%
A 20996
 
10.5%
R 16210
 
8.1%
I 15446
 
7.7%
L 14262
 
7.1%
O 13824
 
6.9%
N 11904
 
5.9%
M 9908
 
4.9%
C 9893
 
4.9%
S 9394
 
4.7%
Other values (47) 57701
28.7%
Common
ValueCountFrequency (%)
25194
63.7%
1 11110
28.1%
2 1117
 
2.8%
- 863
 
2.2%
/ 391
 
1.0%
3 256
 
0.6%
0 154
 
0.4%
4 119
 
0.3%
5 61
 
0.2%
7 60
 
0.2%
Other values (15) 250
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 240331
> 99.9%
None 41
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
25194
 
10.5%
E 21259
 
8.8%
A 20996
 
8.7%
R 16210
 
6.7%
I 15446
 
6.4%
L 14262
 
5.9%
O 13824
 
5.8%
N 11904
 
5.0%
1 11110
 
4.6%
M 9908
 
4.1%
Other values (65) 80218
33.4%
None
ValueCountFrequency (%)
é 23
56.1%
É 7
 
17.1%
Œ 3
 
7.3%
À 3
 
7.3%
ê 2
 
4.9%
à 2
 
4.9%
Ç 1
 
2.4%

IDAr_Couleur
Real number (ℝ)

High correlation  Zeros 

Distinct1617
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.54379
Minimum0
Maximum2123
Zeros7078
Zeros (%)30.7%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-04-18T18:23:09.665711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median156
Q3275
95-th percentile1294
Maximum2123
Range2123
Interquartile range (IQR)275

Descriptive statistics

Standard deviation411.44543
Coefficient of variation (CV)1.4112646
Kurtosis4.6049756
Mean291.54379
Median Absolute Deviation (MAD)156
Skewness2.177307
Sum6724166
Variance169287.34
MonotonicityNot monotonic
2025-04-18T18:23:09.778637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7078
30.7%
122 2386
 
10.3%
275 1512
 
6.6%
156 1080
 
4.7%
191 815
 
3.5%
127 736
 
3.2%
201 337
 
1.5%
90 168
 
0.7%
190 149
 
0.6%
244 141
 
0.6%
Other values (1607) 8662
37.6%
ValueCountFrequency (%)
0 7078
30.7%
2 13
 
0.1%
3 5
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
15 2
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
2123 1
 
< 0.1%
2121 1
 
< 0.1%
2120 1
 
< 0.1%
2119 1
 
< 0.1%
2118 1
 
< 0.1%
2117 3
< 0.1%
2115 1
 
< 0.1%
2114 3
< 0.1%
2112 1
 
< 0.1%
2111 1
 
< 0.1%

IDArFamille
Real number (ℝ)

Distinct41
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.785033
Minimum-1
Maximum56
Zeros219
Zeros (%)0.9%
Negative10
Negative (%)< 0.1%
Memory size360.4 KiB
2025-04-18T18:23:09.876854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2
Q14
median6
Q327
95-th percentile38
Maximum56
Range57
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.233916
Coefficient of variation (CV)1.032563
Kurtosis-0.79547407
Mean13.785033
Median Absolute Deviation (MAD)4
Skewness0.93088456
Sum317938
Variance202.60435
MonotonicityNot monotonic
2025-04-18T18:23:09.970470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
4 3681
16.0%
6 3087
13.4%
2 2645
11.5%
3 1985
8.6%
38 1765
 
7.7%
5 1617
 
7.0%
10 1523
 
6.6%
25 881
 
3.8%
34 729
 
3.2%
37 657
 
2.8%
Other values (31) 4494
19.5%
ValueCountFrequency (%)
-1 10
 
< 0.1%
0 219
 
0.9%
1 526
 
2.3%
2 2645
11.5%
3 1985
8.6%
4 3681
16.0%
5 1617
7.0%
6 3087
13.4%
7 486
 
2.1%
8 36
 
0.2%
ValueCountFrequency (%)
56 1
 
< 0.1%
54 16
 
0.1%
52 1
 
< 0.1%
51 7
 
< 0.1%
49 216
0.9%
48 7
 
< 0.1%
47 4
 
< 0.1%
46 138
0.6%
45 11
 
< 0.1%
44 65
 
0.3%

SaisiPar
Categorical

High correlation  Missing 

Distinct34
Distinct (%)0.5%
Missing16242
Missing (%)70.4%
Memory size1.4 MiB
superviseur
1687 
UP.Ceren
1058 
IBTISSEM
584 
K.tuba
467 
florian.desvign
457 
Other values (29)
2569 

Length

Max length15
Median length14
Mean length9.8131047
Min length4

Characters and Unicode

Total characters66945
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowsuperviseur
2nd rowsuperviseur
3rd rowsuperviseur
4th rowsuperviseur
5th rowsuperviseur

Common Values

ValueCountFrequency (%)
superviseur 1687
 
7.3%
UP.Ceren 1058
 
4.6%
IBTISSEM 584
 
2.5%
K.tuba 467
 
2.0%
florian.desvign 457
 
2.0%
B.zuhal 385
 
1.7%
soraia pereira 351
 
1.5%
ibtissem 339
 
1.5%
s.hayriye 279
 
1.2%
yasmine 187
 
0.8%
Other values (24) 1028
 
4.5%
(Missing) 16242
70.4%

Length

2025-04-18T18:23:10.089064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
superviseur 1711
23.0%
up.ceren 1058
14.3%
ibtissem 923
12.4%
b.zuhal 510
 
6.9%
k.tuba 501
 
6.7%
pereira 492
 
6.6%
soraia 492
 
6.6%
florian.desvign 457
 
6.2%
s.hayriye 279
 
3.8%
yasmine 187
 
2.5%
Other values (19) 813
11.0%

Most occurring characters

ValueCountFrequency (%)
e 8177
 
12.2%
r 6504
 
9.7%
s 5638
 
8.4%
i 5216
 
7.8%
u 4867
 
7.3%
a 4476
 
6.7%
. 3336
 
5.0%
n 2717
 
4.1%
v 2158
 
3.2%
p 2094
 
3.1%
Other values (38) 21762
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51904
77.5%
Uppercase Letter 11104
 
16.6%
Other Punctuation 3336
 
5.0%
Space Separator 601
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8177
15.8%
r 6504
12.5%
s 5638
10.9%
i 5216
10.0%
u 4867
9.4%
a 4476
8.6%
n 2717
 
5.2%
v 2158
 
4.2%
p 2094
 
4.0%
t 1382
 
2.7%
Other values (14) 8675
16.7%
Uppercase Letter
ValueCountFrequency (%)
I 1391
12.5%
S 1367
12.3%
P 1318
11.9%
B 1163
10.5%
U 1106
10.0%
C 1078
9.7%
E 911
8.2%
M 763
6.9%
T 584
5.3%
K 467
 
4.2%
Other values (11) 956
8.6%
Space Separator
ValueCountFrequency (%)
559
93.0%
  42
 
7.0%
Other Punctuation
ValueCountFrequency (%)
. 3336
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63008
94.1%
Common 3937
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8177
13.0%
r 6504
 
10.3%
s 5638
 
8.9%
i 5216
 
8.3%
u 4867
 
7.7%
a 4476
 
7.1%
n 2717
 
4.3%
v 2158
 
3.4%
p 2094
 
3.3%
I 1391
 
2.2%
Other values (35) 19770
31.4%
Common
ValueCountFrequency (%)
. 3336
84.7%
559
 
14.2%
  42
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66903
99.9%
None 42
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8177
 
12.2%
r 6504
 
9.7%
s 5638
 
8.4%
i 5216
 
7.8%
u 4867
 
7.3%
a 4476
 
6.7%
. 3336
 
5.0%
n 2717
 
4.1%
v 2158
 
3.2%
p 2094
 
3.1%
Other values (37) 21720
32.5%
None
ValueCountFrequency (%)
  42
100.0%
Distinct316
Distinct (%)1.4%
Missing154
Missing (%)0.7%
Memory size360.4 KiB
Minimum2023-11-02 00:00:00
Maximum2025-02-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-18T18:23:10.203321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:10.334044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ModifiePar
Categorical

High correlation  Missing 

Distinct35
Distinct (%)0.5%
Missing16670
Missing (%)72.3%
Memory size1.4 MiB
superviseur
1056 
UP.Ceren
845 
H.HANA
537 
IBTISSEM
470 
s.hayriye
454 
Other values (30)
3032 

Length

Max length15
Median length12
Mean length9.3817642
Min length5

Characters and Unicode

Total characters59987
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowsuperviseur
2nd rowsuperviseur
3rd rowsuperviseur
4th rowsuperviseur
5th rowsuperviseur

Common Values

ValueCountFrequency (%)
superviseur 1056
 
4.6%
UP.Ceren 845
 
3.7%
H.HANA 537
 
2.3%
IBTISSEM 470
 
2.0%
s.hayriye 454
 
2.0%
Poobahdee GOVIN 412
 
1.8%
Pooba 406
 
1.8%
florian.desvign 354
 
1.5%
K.tuba 298
 
1.3%
B.zuhal 286
 
1.2%
Other values (25) 1276
 
5.5%
(Missing) 16670
72.3%

Length

2025-04-18T18:23:10.453456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
superviseur 1093
15.1%
up.ceren 845
11.7%
ibtissem 625
 
8.6%
h.hana 537
 
7.4%
s.hayriye 454
 
6.3%
b.zuhal 427
 
5.9%
poobahdee 412
 
5.7%
govin 412
 
5.7%
pooba 406
 
5.6%
florian.desvign 354
 
4.9%
Other values (23) 1675
23.1%

Most occurring characters

ValueCountFrequency (%)
e 6388
 
10.6%
r 4366
 
7.3%
a 4242
 
7.1%
s 3677
 
6.1%
u 3418
 
5.7%
i 3401
 
5.7%
. 3295
 
5.5%
o 2517
 
4.2%
n 2277
 
3.8%
P 1841
 
3.1%
Other values (40) 24565
41.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41545
69.3%
Uppercase Letter 14301
 
23.8%
Other Punctuation 3295
 
5.5%
Space Separator 846
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6388
15.4%
r 4366
10.5%
a 4242
10.2%
s 3677
8.9%
u 3418
8.2%
i 3401
8.2%
o 2517
 
6.1%
n 2277
 
5.5%
h 1577
 
3.8%
b 1573
 
3.8%
Other values (16) 8109
19.5%
Uppercase Letter
ValueCountFrequency (%)
P 1841
12.9%
I 1475
10.3%
A 1160
 
8.1%
S 1111
 
7.8%
H 1074
 
7.5%
N 949
 
6.6%
U 919
 
6.4%
B 855
 
6.0%
C 847
 
5.9%
M 744
 
5.2%
Other values (11) 3326
23.3%
Space Separator
ValueCountFrequency (%)
434
51.3%
  412
48.7%
Other Punctuation
ValueCountFrequency (%)
. 3295
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55846
93.1%
Common 4141
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6388
 
11.4%
r 4366
 
7.8%
a 4242
 
7.6%
s 3677
 
6.6%
u 3418
 
6.1%
i 3401
 
6.1%
o 2517
 
4.5%
n 2277
 
4.1%
P 1841
 
3.3%
h 1577
 
2.8%
Other values (37) 22142
39.6%
Common
ValueCountFrequency (%)
. 3295
79.6%
434
 
10.5%
  412
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59574
99.3%
None 413
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6388
 
10.7%
r 4366
 
7.3%
a 4242
 
7.1%
s 3677
 
6.2%
u 3418
 
5.7%
i 3401
 
5.7%
. 3295
 
5.5%
o 2517
 
4.2%
n 2277
 
3.8%
P 1841
 
3.1%
Other values (38) 24152
40.5%
None
ValueCountFrequency (%)
  412
99.8%
ä 1
 
0.2%

ModifieLe
Date

Missing 

Distinct300
Distinct (%)4.4%
Missing16293
Missing (%)70.6%
Memory size360.4 KiB
Minimum2023-10-31 00:00:00
Maximum2025-02-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-18T18:23:10.563223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:10.707774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Etat
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
23005 
0
 
59

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23064
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23005
99.7%
0 59
 
0.3%

Length

2025-04-18T18:23:10.911185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T18:23:11.007989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23005
99.7%
0 59
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 23005
99.7%
0 59
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23064
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 23005
99.7%
0 59
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23064
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 23005
99.7%
0 59
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 23005
99.7%
0 59
 
0.3%

Prix
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct907
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.705914
Minimum0
Maximum571428.57
Zeros4917
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-04-18T18:23:11.131577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.14
median6.5151
Q311.43
95-th percentile22.85
Maximum571428.57
Range571428.57
Interquartile range (IQR)10.29

Descriptive statistics

Standard deviation5459.294
Coefficient of variation (CV)83.086797
Kurtosis10461.794
Mean65.705914
Median Absolute Deviation (MAD)4.9149
Skewness101.11983
Sum1515441.2
Variance29803890
MonotonicityNot monotonic
2025-04-18T18:23:11.236211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4917
 
21.3%
14.28 760
 
3.3%
11.43 682
 
3.0%
2.83 604
 
2.6%
12.85 517
 
2.2%
8.57 498
 
2.2%
17.14 473
 
2.1%
20 419
 
1.8%
2.26 384
 
1.7%
0.29 379
 
1.6%
Other values (897) 13431
58.2%
ValueCountFrequency (%)
0 4917
21.3%
0.01 142
 
0.6%
0.03 2
 
< 0.1%
0.1 1
 
< 0.1%
0.15 1
 
< 0.1%
0.17 4
 
< 0.1%
0.2 1
 
< 0.1%
0.25 1
 
< 0.1%
0.29 379
 
1.6%
0.3 1
 
< 0.1%
ValueCountFrequency (%)
571428.57 2
 
< 0.1%
185478.55 1
 
< 0.1%
89.99 1
 
< 0.1%
88.8 1
 
< 0.1%
85.71 1
 
< 0.1%
80 1
 
< 0.1%
71.43 1
 
< 0.1%
65.71 1
 
< 0.1%
57.14 5
< 0.1%
54.29 1
 
< 0.1%

TauxTVA
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0.0
19335 
-1.0
2702 
0.2
 
1015
0.21
 
12

Length

Max length4
Median length3
Mean length3.1176726
Min length3

Characters and Unicode

Total characters71906
Distinct characters5
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row-1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 19335
83.8%
-1.0 2702
 
11.7%
0.2 1015
 
4.4%
0.21 12
 
0.1%

Length

2025-04-18T18:23:11.343513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T18:23:11.413788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 19335
83.8%
1.0 2702
 
11.7%
0.2 1015
 
4.4%
0.21 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 42399
59.0%
. 23064
32.1%
1 2714
 
3.8%
- 2702
 
3.8%
2 1027
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46140
64.2%
Other Punctuation 23064
32.1%
Dash Punctuation 2702
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42399
91.9%
1 2714
 
5.9%
2 1027
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 23064
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2702
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71906
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42399
59.0%
. 23064
32.1%
1 2714
 
3.8%
- 2702
 
3.8%
2 1027
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42399
59.0%
. 23064
32.1%
1 2714
 
3.8%
- 2702
 
3.8%
2 1027
 
1.4%

Image
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
None
23064 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters92256
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 23064
100.0%

Length

2025-04-18T18:23:11.499018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T18:23:11.552851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
none 23064
100.0%

Most occurring characters

ValueCountFrequency (%)
N 23064
25.0%
o 23064
25.0%
n 23064
25.0%
e 23064
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 69192
75.0%
Uppercase Letter 23064
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 23064
33.3%
n 23064
33.3%
e 23064
33.3%
Uppercase Letter
ValueCountFrequency (%)
N 23064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 92256
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 23064
25.0%
o 23064
25.0%
n 23064
25.0%
e 23064
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 23064
25.0%
o 23064
25.0%
n 23064
25.0%
e 23064
25.0%

Composition
Text

Missing 

Distinct530
Distinct (%)4.1%
Missing9997
Missing (%)43.3%
Memory size1.2 MiB
2025-04-18T18:23:11.970611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length49
Mean length10.106298
Min length3

Characters and Unicode

Total characters132059
Distinct characters81
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique157 ?
Unique (%)1.2%

Sample

1st row96% POLYESTER 4% ELASTANE WOVEN
2nd rowTP:100% PES
3rd row66%Acrylique 18%Polyamide 8%Laine
4th row40% ACR 30% PA 30% MOHAIR
5th row40% ACR 30% PA 30% MOHAIR
ValueCountFrequency (%)
polyester 4361
22.2%
coton 2088
 
10.6%
viscose 1968
 
10.0%
100 801
 
4.1%
polyurethane 674
 
3.4%
acrylique 641
 
3.3%
pes 446
 
2.3%
cuir 286
 
1.5%
polyamide 279
 
1.4%
bi-matière 257
 
1.3%
Other values (535) 7851
40.0%
2025-04-18T18:23:12.533657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 16743
12.7%
O 13280
 
10.1%
S 10164
 
7.7%
L 8396
 
6.4%
T 8297
 
6.3%
P 7029
 
5.3%
R 6828
 
5.2%
6645
 
5.0%
Y 6476
 
4.9%
C 6441
 
4.9%
Other values (71) 41760
31.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 106067
80.3%
Decimal Number 11272
 
8.5%
Space Separator 6663
 
5.0%
Other Punctuation 6046
 
4.6%
Lowercase Letter 1665
 
1.3%
Dash Punctuation 275
 
0.2%
Control 62
 
< 0.1%
Other Symbol 4
 
< 0.1%
Math Symbol 4
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 16743
15.8%
O 13280
12.5%
S 10164
9.6%
L 8396
7.9%
T 8297
7.8%
P 7029
 
6.6%
R 6828
 
6.4%
Y 6476
 
6.1%
C 6441
 
6.1%
I 4835
 
4.6%
Other values (18) 17578
16.6%
Lowercase Letter
ValueCountFrequency (%)
l 180
10.8%
o 179
10.8%
e 174
10.5%
y 162
9.7%
i 123
 
7.4%
c 121
 
7.3%
r 119
 
7.1%
n 110
 
6.6%
a 86
 
5.2%
s 79
 
4.7%
Other values (15) 332
19.9%
Decimal Number
ValueCountFrequency (%)
0 3885
34.5%
1 1967
17.5%
5 936
 
8.3%
2 919
 
8.2%
4 836
 
7.4%
3 662
 
5.9%
8 587
 
5.2%
9 569
 
5.0%
6 459
 
4.1%
7 452
 
4.0%
Other Punctuation
ValueCountFrequency (%)
% 5371
88.8%
, 353
 
5.8%
/ 153
 
2.5%
: 92
 
1.5%
? 53
 
0.9%
. 14
 
0.2%
& 4
 
0.1%
" 4
 
0.1%
; 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
6645
99.7%
  18
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 270
98.2%
5
 
1.8%
Control
ValueCountFrequency (%)
34
54.8%
28
45.2%
Other Symbol
ValueCountFrequency (%)
° 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%
Modifier Symbol
ValueCountFrequency (%)
¨ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 107732
81.6%
Common 24327
 
18.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 16743
15.5%
O 13280
12.3%
S 10164
9.4%
L 8396
7.8%
T 8297
7.7%
P 7029
 
6.5%
R 6828
 
6.3%
Y 6476
 
6.0%
C 6441
 
6.0%
I 4835
 
4.5%
Other values (43) 19243
17.9%
Common
ValueCountFrequency (%)
6645
27.3%
% 5371
22.1%
0 3885
16.0%
1 1967
 
8.1%
5 936
 
3.8%
2 919
 
3.8%
4 836
 
3.4%
3 662
 
2.7%
8 587
 
2.4%
9 569
 
2.3%
Other values (18) 1950
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 131749
99.8%
None 305
 
0.2%
Punctuation 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 16743
12.7%
O 13280
 
10.1%
S 10164
 
7.7%
L 8396
 
6.4%
T 8297
 
6.3%
P 7029
 
5.3%
R 6828
 
5.2%
6645
 
5.0%
Y 6476
 
4.9%
C 6441
 
4.9%
Other values (63) 41450
31.5%
None
ValueCountFrequency (%)
È 274
89.8%
  18
 
5.9%
É 5
 
1.6%
° 4
 
1.3%
ê 2
 
0.7%
é 1
 
0.3%
¨ 1
 
0.3%
Punctuation
ValueCountFrequency (%)
5
100.0%

PrixFac
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct242
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean241.32588
Minimum0
Maximum2000000
Zeros3088
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-04-18T18:23:12.662107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.9
median37.99
Q354.99
95-th percentile89.99
Maximum2000000
Range2000000
Interquartile range (IQR)40.09

Descriptive statistics

Standard deviation19107.416
Coefficient of variation (CV)79.176823
Kurtosis10461.801
Mean241.32588
Median Absolute Deviation (MAD)20
Skewness101.11984
Sum5565940
Variance3.6509335 × 108
MonotonicityNot monotonic
2025-04-18T18:23:12.806955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3088
 
13.4%
49.99 1476
 
6.4%
39.99 1407
 
6.1%
44.99 967
 
4.2%
59.99 962
 
4.2%
29.99 955
 
4.1%
69.99 808
 
3.5%
19.99 720
 
3.1%
54.99 666
 
2.9%
9.9 635
 
2.8%
Other values (232) 11380
49.3%
ValueCountFrequency (%)
0 3088
13.4%
0.01 151
 
0.7%
0.1 2
 
< 0.1%
0.15 1
 
< 0.1%
0.2 1
 
< 0.1%
0.25 1
 
< 0.1%
0.5 1
 
< 0.1%
0.6 1
 
< 0.1%
0.8 1
 
< 0.1%
1 624
 
2.7%
ValueCountFrequency (%)
2000000 2
< 0.1%
649174.94 1
 
< 0.1%
350 1
 
< 0.1%
299.99 2
< 0.1%
280 1
 
< 0.1%
260 1
 
< 0.1%
249.99 3
< 0.1%
245 1
 
< 0.1%
239.99 1
 
< 0.1%
229 3
< 0.1%

PoidsBrut
Real number (ℝ)

Skewed  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0073339403
Minimum0
Maximum99
Zeros22996
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-04-18T18:23:12.906713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.65717127
Coefficient of variation (CV)89.606847
Kurtosis22329.656
Mean0.0073339403
Median Absolute Deviation (MAD)0
Skewness148.37809
Sum169.15
Variance0.43187408
MonotonicityNot monotonic
2025-04-18T18:23:12.965749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 22996
99.7%
1 56
 
0.2%
0.25 4
 
< 0.1%
0.3 3
 
< 0.1%
2 1
 
< 0.1%
0.15 1
 
< 0.1%
10 1
 
< 0.1%
99 1
 
< 0.1%
0.1 1
 
< 0.1%
ValueCountFrequency (%)
0 22996
99.7%
0.1 1
 
< 0.1%
0.15 1
 
< 0.1%
0.25 4
 
< 0.1%
0.3 3
 
< 0.1%
1 56
 
0.2%
2 1
 
< 0.1%
10 1
 
< 0.1%
99 1
 
< 0.1%
ValueCountFrequency (%)
99 1
 
< 0.1%
10 1
 
< 0.1%
2 1
 
< 0.1%
1 56
 
0.2%
0.3 3
 
< 0.1%
0.25 4
 
< 0.1%
0.15 1
 
< 0.1%
0.1 1
 
< 0.1%
0 22996
99.7%

PoidsNet
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct299
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24314156
Minimum0
Maximum810
Zeros8277
Zeros (%)35.9%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-04-18T18:23:13.058935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1
Q30.3
95-th percentile0.6
Maximum810
Range810
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation6.3783769
Coefficient of variation (CV)26.233182
Kurtosis13326.922
Mean0.24314156
Median Absolute Deviation (MAD)0.1
Skewness113.57317
Sum5607.817
Variance40.683692
MonotonicityNot monotonic
2025-04-18T18:23:13.187387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8277
35.9%
0.2 1427
 
6.2%
0.1 1286
 
5.6%
0.15 1118
 
4.8%
0.35 732
 
3.2%
0.25 676
 
2.9%
0.3 623
 
2.7%
0.4 425
 
1.8%
0.45 296
 
1.3%
0.05 257
 
1.1%
Other values (289) 7947
34.5%
ValueCountFrequency (%)
0 8277
35.9%
0.001 22
 
0.1%
0.002 20
 
0.1%
0.003 51
 
0.2%
0.004 100
 
0.4%
0.005 162
 
0.7%
0.006 121
 
0.5%
0.007 92
 
0.4%
0.008 112
 
0.5%
0.009 67
 
0.3%
ValueCountFrequency (%)
810 1
< 0.1%
530 1
< 0.1%
24 1
< 0.1%
1.83 1
< 0.1%
1.81 1
< 0.1%
1.8 1
< 0.1%
1.75 1
< 0.1%
1.74 1
< 0.1%
1.73 2
< 0.1%
1.72 2
< 0.1%

IDArSousFamille
Real number (ℝ)

Zeros 

Distinct126
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.61867
Minimum-1
Maximum336
Zeros463
Zeros (%)2.0%
Negative251
Negative (%)1.1%
Memory size360.4 KiB
2025-04-18T18:23:13.320710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile4
Q1195
median211
Q3221
95-th percentile282
Maximum336
Range337
Interquartile range (IQR)26

Descriptive statistics

Standard deviation74.752959
Coefficient of variation (CV)0.38808781
Kurtosis2.3442742
Mean192.61867
Median Absolute Deviation (MAD)12
Skewness-1.7931534
Sum4442557
Variance5588.0048
MonotonicityNot monotonic
2025-04-18T18:23:13.441928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211 2220
 
9.6%
4 1498
 
6.5%
216 1379
 
6.0%
201 1364
 
5.9%
191 1328
 
5.8%
221 1265
 
5.5%
212 857
 
3.7%
218 684
 
3.0%
208 607
 
2.6%
223 604
 
2.6%
Other values (116) 11258
48.8%
ValueCountFrequency (%)
-1 251
 
1.1%
0 463
 
2.0%
1 377
 
1.6%
4 1498
6.5%
5 19
 
0.1%
6 1
 
< 0.1%
7 29
 
0.1%
8 178
 
0.8%
9 3
 
< 0.1%
190 483
 
2.1%
ValueCountFrequency (%)
336 1
 
< 0.1%
335 1
 
< 0.1%
333 5
 
< 0.1%
332 1
 
< 0.1%
331 2
 
< 0.1%
329 14
0.1%
325 24
0.1%
323 4
 
< 0.1%
322 2
 
< 0.1%
321 14
0.1%

IDGrille
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7289282
Minimum0
Maximum11
Zeros429
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-04-18T18:23:13.526623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0203934
Coefficient of variation (CV)0.59018844
Kurtosis9.001478
Mean1.7289282
Median Absolute Deviation (MAD)1
Skewness2.1281447
Sum39876
Variance1.0412028
MonotonicityNot monotonic
2025-04-18T18:23:13.590673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 11291
49.0%
2 6981
30.3%
3 3727
 
16.2%
0 429
 
1.9%
4 320
 
1.4%
7 192
 
0.8%
6 100
 
0.4%
10 16
 
0.1%
5 4
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 429
 
1.9%
1 11291
49.0%
2 6981
30.3%
3 3727
 
16.2%
4 320
 
1.4%
5 4
 
< 0.1%
6 100
 
0.4%
7 192
 
0.8%
8 2
 
< 0.1%
10 16
 
0.1%
ValueCountFrequency (%)
11 2
 
< 0.1%
10 16
 
0.1%
8 2
 
< 0.1%
7 192
 
0.8%
6 100
 
0.4%
5 4
 
< 0.1%
4 320
 
1.4%
3 3727
 
16.2%
2 6981
30.3%
1 11291
49.0%

Reference
Text

Missing 

Distinct4400
Distinct (%)61.3%
Missing15891
Missing (%)68.9%
Memory size1.0 MiB
2025-04-18T18:23:13.860611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length7
Mean length7.6818625
Min length3

Characters and Unicode

Total characters55102
Distinct characters53
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3073 ?
Unique (%)42.8%

Sample

1st row42624_24K
2nd rowLOUISON C1
3rd rowNATALIA
4th rowAENU102A
5th rowAENU102
ValueCountFrequency (%)
c1 392
 
4.2%
r1 350
 
3.8%
p1 183
 
2.0%
t1 182
 
2.0%
j1 149
 
1.6%
ml 93
 
1.0%
v1 85
 
0.9%
sh1 78
 
0.8%
g1 56
 
0.6%
d1 51
 
0.5%
Other values (4071) 7694
82.6%
2025-04-18T18:23:14.227833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 5814
 
10.6%
E 3933
 
7.1%
1 3805
 
6.9%
C 3189
 
5.8%
A 3108
 
5.6%
H 2988
 
5.4%
0 2623
 
4.8%
2142
 
3.9%
R 2104
 
3.8%
2 1980
 
3.6%
Other values (43) 23416
42.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 35981
65.3%
Decimal Number 16630
30.2%
Space Separator 2142
 
3.9%
Dash Punctuation 239
 
0.4%
Lowercase Letter 68
 
0.1%
Connector Punctuation 41
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 5814
16.2%
E 3933
10.9%
C 3189
 
8.9%
A 3108
 
8.6%
H 2988
 
8.3%
R 2104
 
5.8%
B 1977
 
5.5%
D 1340
 
3.7%
T 1329
 
3.7%
L 1310
 
3.6%
Other values (16) 8889
24.7%
Lowercase Letter
ValueCountFrequency (%)
e 16
23.5%
t 16
23.5%
s 13
19.1%
a 5
 
7.4%
n 4
 
5.9%
o 3
 
4.4%
h 2
 
2.9%
r 2
 
2.9%
l 2
 
2.9%
b 2
 
2.9%
Other values (3) 3
 
4.4%
Decimal Number
ValueCountFrequency (%)
1 3805
22.9%
0 2623
15.8%
2 1980
11.9%
4 1672
10.1%
3 1295
 
7.8%
5 1250
 
7.5%
6 1162
 
7.0%
7 970
 
5.8%
9 937
 
5.6%
8 936
 
5.6%
Space Separator
ValueCountFrequency (%)
2142
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 239
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 41
100.0%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36049
65.4%
Common 19053
34.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 5814
16.1%
E 3933
10.9%
C 3189
 
8.8%
A 3108
 
8.6%
H 2988
 
8.3%
R 2104
 
5.8%
B 1977
 
5.5%
D 1340
 
3.7%
T 1329
 
3.7%
L 1310
 
3.6%
Other values (29) 8957
24.8%
Common
ValueCountFrequency (%)
1 3805
20.0%
0 2623
13.8%
2142
11.2%
2 1980
10.4%
4 1672
8.8%
3 1295
 
6.8%
5 1250
 
6.6%
6 1162
 
6.1%
7 970
 
5.1%
9 937
 
4.9%
Other values (4) 1217
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 5814
 
10.6%
E 3933
 
7.1%
1 3805
 
6.9%
C 3189
 
5.8%
A 3108
 
5.6%
H 2988
 
5.4%
0 2623
 
4.8%
2142
 
3.9%
R 2104
 
3.8%
2 1980
 
3.6%
Other values (43) 23416
42.5%

NomenclatureValide
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
23061 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23064
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23061
> 99.9%
1 3
 
< 0.1%

Length

2025-04-18T18:23:14.304253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T18:23:14.357662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23061
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 23061
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23064
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23061
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23064
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23061
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23061
> 99.9%
1 3
 
< 0.1%

IDSaison
Real number (ℝ)

High correlation  Zeros 

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.689863
Minimum0
Maximum34
Zeros766
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-04-18T18:23:14.432121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median17
Q321
95-th percentile28
Maximum34
Range34
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.4880866
Coefficient of variation (CV)0.69307389
Kurtosis-1.527797
Mean13.689863
Median Absolute Deviation (MAD)10
Skewness-0.021571643
Sum315743
Variance90.023787
MonotonicityNot monotonic
2025-04-18T18:23:14.535939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
4 2780
12.1%
5 2032
 
8.8%
1 1714
 
7.4%
19 1599
 
6.9%
18 1561
 
6.8%
22 1534
 
6.7%
28 1504
 
6.5%
3 1477
 
6.4%
15 1419
 
6.2%
20 1410
 
6.1%
Other values (22) 6034
26.2%
ValueCountFrequency (%)
0 766
 
3.3%
1 1714
7.4%
2 160
 
0.7%
3 1477
6.4%
4 2780
12.1%
5 2032
8.8%
6 887
 
3.8%
8 1
 
< 0.1%
11 14
 
0.1%
12 16
 
0.1%
ValueCountFrequency (%)
34 62
 
0.3%
33 4
 
< 0.1%
32 4
 
< 0.1%
31 2
 
< 0.1%
30 2
 
< 0.1%
29 7
 
< 0.1%
28 1504
6.5%
27 1178
5.1%
26 2
 
< 0.1%
25 1
 
< 0.1%

NumInterne
Real number (ℝ)

High correlation  Zeros 

Distinct283
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.823578
Minimum0
Maximum282
Zeros15119
Zeros (%)65.6%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-04-18T18:23:14.643458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q350
95-th percentile145
Maximum282
Range282
Interquartile range (IQR)50

Descriptive statistics

Standard deviation63.123099
Coefficient of variation (CV)1.7142033
Kurtosis0.69660815
Mean36.823578
Median Absolute Deviation (MAD)0
Skewness1.4463253
Sum849299
Variance3984.5257
MonotonicityNot monotonic
2025-04-18T18:23:14.941270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15119
65.6%
145 3548
 
15.4%
1 135
 
0.6%
90 123
 
0.5%
2 99
 
0.4%
3 86
 
0.4%
4 82
 
0.4%
5 82
 
0.4%
6 79
 
0.3%
7 67
 
0.3%
Other values (273) 3644
 
15.8%
ValueCountFrequency (%)
0 15119
65.6%
1 135
 
0.6%
2 99
 
0.4%
3 86
 
0.4%
4 82
 
0.4%
5 82
 
0.4%
6 79
 
0.3%
7 67
 
0.3%
8 58
 
0.3%
9 63
 
0.3%
ValueCountFrequency (%)
282 1
 
< 0.1%
281 1
 
< 0.1%
280 1
 
< 0.1%
279 1
 
< 0.1%
278 1
 
< 0.1%
277 1
 
< 0.1%
276 2
< 0.1%
275 2
< 0.1%
274 4
< 0.1%
273 2
< 0.1%

IDPatronnage
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
15515 
-1
7549 

Length

Max length2
Median length1
Mean length1.3273066
Min length1

Characters and Unicode

Total characters30613
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
0 15515
67.3%
-1 7549
32.7%

Length

2025-04-18T18:23:15.058690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T18:23:15.145446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15515
67.3%
1 7549
32.7%

Most occurring characters

ValueCountFrequency (%)
0 15515
50.7%
- 7549
24.7%
1 7549
24.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23064
75.3%
Dash Punctuation 7549
 
24.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15515
67.3%
1 7549
32.7%
Dash Punctuation
ValueCountFrequency (%)
- 7549
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30613
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15515
50.7%
- 7549
24.7%
1 7549
24.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15515
50.7%
- 7549
24.7%
1 7549
24.7%

IDFournisseur
Real number (ℝ)

High correlation  Zeros 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.952133
Minimum-1
Maximum709
Zeros15825
Zeros (%)68.6%
Negative15
Negative (%)0.1%
Memory size360.4 KiB
2025-04-18T18:23:15.239949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q3213
95-th percentile289
Maximum709
Range710
Interquartile range (IQR)213

Descriptive statistics

Standard deviation119.77408
Coefficient of variation (CV)1.5564751
Kurtosis0.37817513
Mean76.952133
Median Absolute Deviation (MAD)0
Skewness1.203181
Sum1774824
Variance14345.83
MonotonicityNot monotonic
2025-04-18T18:23:15.352695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 15825
68.6%
213 4942
 
21.4%
289 1439
 
6.2%
440 434
 
1.9%
310 74
 
0.3%
283 62
 
0.3%
281 44
 
0.2%
48 35
 
0.2%
285 34
 
0.1%
328 20
 
0.1%
Other values (19) 155
 
0.7%
ValueCountFrequency (%)
-1 15
 
0.1%
0 15825
68.6%
25 2
 
< 0.1%
32 8
 
< 0.1%
48 35
 
0.2%
64 3
 
< 0.1%
107 14
 
0.1%
213 4942
 
21.4%
278 3
 
< 0.1%
279 16
 
0.1%
ValueCountFrequency (%)
709 8
 
< 0.1%
676 5
 
< 0.1%
588 1
 
< 0.1%
523 9
 
< 0.1%
522 5
 
< 0.1%
440 434
1.9%
384 2
 
< 0.1%
360 13
 
0.1%
328 20
 
0.1%
311 15
 
0.1%

AppliqueFodec
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
15514 
1
7550 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23064
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 15514
67.3%
1 7550
32.7%

Length

2025-04-18T18:23:15.446815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T18:23:15.491130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15514
67.3%
1 7550
32.7%

Most occurring characters

ValueCountFrequency (%)
0 15514
67.3%
1 7550
32.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23064
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15514
67.3%
1 7550
32.7%

Most occurring scripts

ValueCountFrequency (%)
Common 23064
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15514
67.3%
1 7550
32.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15514
67.3%
1 7550
32.7%

TypeArticleService
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
21373 
255
 
1642
1
 
49

Length

Max length3
Median length1
Mean length1.1423864
Min length1

Characters and Unicode

Total characters26348
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21373
92.7%
255 1642
 
7.1%
1 49
 
0.2%

Length

2025-04-18T18:23:15.557388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T18:23:15.610948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 21373
92.7%
255 1642
 
7.1%
1 49
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 21373
81.1%
5 3284
 
12.5%
2 1642
 
6.2%
1 49
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21373
81.1%
5 3284
 
12.5%
2 1642
 
6.2%
1 49
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 26348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21373
81.1%
5 3284
 
12.5%
2 1642
 
6.2%
1 49
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21373
81.1%
5 3284
 
12.5%
2 1642
 
6.2%
1 49
 
0.2%

DateValidationNomenclature
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
None
23061 
2024-05-03
 
1
2024-07-20
 
1
2024-08-12
 
1

Length

Max length10
Median length4
Mean length4.0007804
Min length4

Characters and Unicode

Total characters92274
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 23061
> 99.9%
2024-05-03 1
 
< 0.1%
2024-07-20 1
 
< 0.1%
2024-08-12 1
 
< 0.1%

Length

2025-04-18T18:23:15.675415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T18:23:15.730326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
none 23061
> 99.9%
2024-05-03 1
 
< 0.1%
2024-07-20 1
 
< 0.1%
2024-08-12 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 23061
25.0%
o 23061
25.0%
n 23061
25.0%
e 23061
25.0%
2 8
 
< 0.1%
0 8
 
< 0.1%
- 6
 
< 0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
3 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 69183
75.0%
Uppercase Letter 23061
 
25.0%
Decimal Number 24
 
< 0.1%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8
33.3%
0 8
33.3%
4 3
 
12.5%
5 1
 
4.2%
3 1
 
4.2%
7 1
 
4.2%
8 1
 
4.2%
1 1
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
o 23061
33.3%
n 23061
33.3%
e 23061
33.3%
Uppercase Letter
ValueCountFrequency (%)
N 23061
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 92244
> 99.9%
Common 30
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8
26.7%
0 8
26.7%
- 6
20.0%
4 3
 
10.0%
5 1
 
3.3%
3 1
 
3.3%
7 1
 
3.3%
8 1
 
3.3%
1 1
 
3.3%
Latin
ValueCountFrequency (%)
N 23061
25.0%
o 23061
25.0%
n 23061
25.0%
e 23061
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 23061
25.0%
o 23061
25.0%
n 23061
25.0%
e 23061
25.0%
2 8
 
< 0.1%
0 8
 
< 0.1%
- 6
 
< 0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
3 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
Distinct14059
Distinct (%)61.2%
Missing106
Missing (%)0.5%
Memory size1.4 MiB
2025-04-18T18:23:15.962459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length176
Median length132
Mean length8.3890147
Min length2

Characters and Unicode

Total characters192595
Distinct characters80
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12409 ?
Unique (%)54.1%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone
ValueCountFrequency (%)
none 4755
 
12.0%
r1 2217
 
5.6%
c1 1359
 
3.4%
j1 940
 
2.4%
p1 922
 
2.3%
bo 894
 
2.3%
ml 809
 
2.0%
mc 802
 
2.0%
t1 571
 
1.4%
sm 456
 
1.1%
Other values (9440) 25976
65.4%
2025-04-18T18:23:16.560326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16763
 
8.7%
A 15743
 
8.2%
E 14756
 
7.7%
N 12870
 
6.7%
R 10858
 
5.6%
L 10831
 
5.6%
I 10445
 
5.4%
O 10051
 
5.2%
1 7649
 
4.0%
M 7277
 
3.8%
Other values (70) 75352
39.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 148273
77.0%
Lowercase Letter 17979
 
9.3%
Space Separator 16763
 
8.7%
Decimal Number 8669
 
4.5%
Dash Punctuation 608
 
0.3%
Other Punctuation 295
 
0.2%
Control 6
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15743
 
10.6%
E 14756
 
10.0%
N 12870
 
8.7%
R 10858
 
7.3%
L 10831
 
7.3%
I 10445
 
7.0%
O 10051
 
6.8%
M 7277
 
4.9%
C 7133
 
4.8%
S 6793
 
4.6%
Other values (20) 41516
28.0%
Lowercase Letter
ValueCountFrequency (%)
e 5348
29.7%
n 5037
28.0%
o 5014
27.9%
a 353
 
2.0%
s 292
 
1.6%
l 249
 
1.4%
t 215
 
1.2%
r 201
 
1.1%
c 199
 
1.1%
u 187
 
1.0%
Other values (18) 884
 
4.9%
Decimal Number
ValueCountFrequency (%)
1 7649
88.2%
2 754
 
8.7%
3 117
 
1.3%
8 29
 
0.3%
7 28
 
0.3%
4 27
 
0.3%
5 24
 
0.3%
0 24
 
0.3%
6 15
 
0.2%
9 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 153
51.9%
? 68
23.1%
. 31
 
10.5%
, 17
 
5.8%
& 16
 
5.4%
' 5
 
1.7%
% 5
 
1.7%
Control
ValueCountFrequency (%)
3
50.0%
3
50.0%
Space Separator
ValueCountFrequency (%)
16763
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 608
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 166252
86.3%
Common 26343
 
13.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15743
 
9.5%
E 14756
 
8.9%
N 12870
 
7.7%
R 10858
 
6.5%
L 10831
 
6.5%
I 10445
 
6.3%
O 10051
 
6.0%
M 7277
 
4.4%
C 7133
 
4.3%
S 6793
 
4.1%
Other values (48) 59495
35.8%
Common
ValueCountFrequency (%)
16763
63.6%
1 7649
29.0%
2 754
 
2.9%
- 608
 
2.3%
/ 153
 
0.6%
3 117
 
0.4%
? 68
 
0.3%
. 31
 
0.1%
8 29
 
0.1%
7 28
 
0.1%
Other values (12) 143
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 192552
> 99.9%
None 43
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16763
 
8.7%
A 15743
 
8.2%
E 14756
 
7.7%
N 12870
 
6.7%
R 10858
 
5.6%
L 10831
 
5.6%
I 10445
 
5.4%
O 10051
 
5.2%
1 7649
 
4.0%
M 7277
 
3.8%
Other values (64) 75309
39.1%
None
ValueCountFrequency (%)
é 17
39.5%
É 8
18.6%
à 7
16.3%
Œ 5
 
11.6%
Ä 4
 
9.3%
À 2
 
4.7%

PrixAchat
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1225
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.902622
Minimum0
Maximum571428.57
Zeros2249
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-04-18T18:23:16.723615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.586
median8.57
Q314
95-th percentile24.57
Maximum571428.57
Range571428.57
Interquartile range (IQR)9.414

Descriptive statistics

Standard deviation5459.2719
Coefficient of variation (CV)80.398542
Kurtosis10461.801
Mean67.902622
Median Absolute Deviation (MAD)4.31
Skewness101.11985
Sum1566106.1
Variance29803649
MonotonicityNot monotonic
2025-04-18T18:23:16.848045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2249
 
9.8%
14.28 852
 
3.7%
11.43 809
 
3.5%
8.57 612
 
2.7%
2.83 607
 
2.6%
12.85 596
 
2.6%
17.14 518
 
2.2%
10 515
 
2.2%
5.71 474
 
2.1%
20 472
 
2.0%
Other values (1215) 15360
66.6%
ValueCountFrequency (%)
0 2249
9.8%
0.01 105
 
0.5%
0.03 2
 
< 0.1%
0.1 5
 
< 0.1%
0.228 1
 
< 0.1%
0.29 350
 
1.5%
0.54 1
 
< 0.1%
0.57 1
 
< 0.1%
0.83 1
 
< 0.1%
0.836 1
 
< 0.1%
ValueCountFrequency (%)
571428.57 2
< 0.1%
185478.55 1
 
< 0.1%
143.7 1
 
< 0.1%
133.95 1
 
< 0.1%
120.43 1
 
< 0.1%
115.55 3
< 0.1%
110.28 1
 
< 0.1%
110.1 1
 
< 0.1%
98.7 3
< 0.1%
93.4 3
< 0.1%

PieceCarton
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
15514 
255
6851 
1
 
589
2
 
110

Length

Max length3
Median length1
Mean length1.594086
Min length1

Characters and Unicode

Total characters36766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row255
2nd row255
3rd row255
4th row255
5th row255

Common Values

ValueCountFrequency (%)
0 15514
67.3%
255 6851
29.7%
1 589
 
2.6%
2 110
 
0.5%

Length

2025-04-18T18:23:16.984138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T18:23:17.107814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15514
67.3%
255 6851
29.7%
1 589
 
2.6%
2 110
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 15514
42.2%
5 13702
37.3%
2 6961
18.9%
1 589
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15514
42.2%
5 13702
37.3%
2 6961
18.9%
1 589
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 36766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15514
42.2%
5 13702
37.3%
2 6961
18.9%
1 589
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15514
42.2%
5 13702
37.3%
2 6961
18.9%
1 589
 
1.6%

IDPays
Real number (ℝ)

High correlation  Zeros 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.417404
Minimum-1
Maximum207
Zeros430
Zeros (%)1.9%
Negative721
Negative (%)3.1%
Memory size360.4 KiB
2025-04-18T18:23:17.249598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q128
median103
Q3172
95-th percentile176
Maximum207
Range208
Interquartile range (IQR)144

Descriptive statistics

Standard deviation71.443302
Coefficient of variation (CV)0.71861967
Kurtosis-1.5756919
Mean99.417404
Median Absolute Deviation (MAD)69
Skewness-0.2848526
Sum2292963
Variance5104.1454
MonotonicityNot monotonic
2025-04-18T18:23:17.409580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 4343
18.8%
172 4340
18.8%
103 3498
15.2%
176 2398
10.4%
35 2074
9.0%
171 1524
 
6.6%
165 1254
 
5.4%
107 1005
 
4.4%
-1 721
 
3.1%
99 522
 
2.3%
Other values (16) 1385
 
6.0%
ValueCountFrequency (%)
-1 721
 
3.1%
0 430
 
1.9%
1 4343
18.8%
2 1
 
< 0.1%
13 78
 
0.3%
18 166
 
0.7%
27 18
 
0.1%
28 418
 
1.8%
29 3
 
< 0.1%
35 2074
9.0%
ValueCountFrequency (%)
207 9
 
< 0.1%
177 17
 
0.1%
176 2398
10.4%
174 29
 
0.1%
173 177
 
0.8%
172 4340
18.8%
171 1524
 
6.6%
165 1254
 
5.4%
152 8
 
< 0.1%
128 4
 
< 0.1%
Distinct109
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-04-18T18:23:17.682292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length4
Mean length5.19641
Min length4

Characters and Unicode

Total characters119850
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.1%

Sample

1st row2024-10-04
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone
ValueCountFrequency (%)
none 18465
80.1%
2024-10-25 425
 
1.8%
2025-04-01 222
 
1.0%
2024-10-11 162
 
0.7%
2025-02-21 156
 
0.7%
2024-10-18 153
 
0.7%
2024-10-04 150
 
0.7%
2025-02-15 146
 
0.6%
2025-05-01 144
 
0.6%
2025-04-15 134
 
0.6%
Other values (99) 2907
 
12.6%
2025-04-18T18:23:17.970765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 18465
15.4%
o 18465
15.4%
n 18465
15.4%
e 18465
15.4%
2 11163
9.3%
0 10481
8.7%
- 9198
7.7%
1 5674
 
4.7%
5 3572
 
3.0%
4 3301
 
2.8%
Other values (5) 2601
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55395
46.2%
Decimal Number 36792
30.7%
Uppercase Letter 18465
 
15.4%
Dash Punctuation 9198
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 11163
30.3%
0 10481
28.5%
1 5674
15.4%
5 3572
 
9.7%
4 3301
 
9.0%
9 706
 
1.9%
8 617
 
1.7%
7 568
 
1.5%
3 518
 
1.4%
6 192
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
o 18465
33.3%
n 18465
33.3%
e 18465
33.3%
Uppercase Letter
ValueCountFrequency (%)
N 18465
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9198
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 73860
61.6%
Common 45990
38.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 11163
24.3%
0 10481
22.8%
- 9198
20.0%
1 5674
12.3%
5 3572
 
7.8%
4 3301
 
7.2%
9 706
 
1.5%
8 617
 
1.3%
7 568
 
1.2%
3 518
 
1.1%
Latin
ValueCountFrequency (%)
N 18465
25.0%
o 18465
25.0%
n 18465
25.0%
e 18465
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 18465
15.4%
o 18465
15.4%
n 18465
15.4%
e 18465
15.4%
2 11163
9.3%
0 10481
8.7%
- 9198
7.7%
1 5674
 
4.7%
5 3572
 
3.0%
4 3301
 
2.8%
Other values (5) 2601
 
2.2%

IDUsine
Real number (ℝ)

High correlation  Zeros 

Distinct67
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.583593
Minimum0
Maximum225
Zeros16498
Zeros (%)71.5%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-04-18T18:23:18.056203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q359
95-th percentile172
Maximum225
Range225
Interquartile range (IQR)59

Descriptive statistics

Standard deviation54.936583
Coefficient of variation (CV)1.8569949
Kurtosis2.6405196
Mean29.583593
Median Absolute Deviation (MAD)0
Skewness1.8951739
Sum682316
Variance3018.0281
MonotonicityNot monotonic
2025-04-18T18:23:18.166858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16498
71.5%
59 3063
 
13.3%
97 1106
 
4.8%
202 439
 
1.9%
172 313
 
1.4%
158 200
 
0.9%
136 184
 
0.8%
98 106
 
0.5%
212 100
 
0.4%
131 92
 
0.4%
Other values (57) 963
 
4.2%
ValueCountFrequency (%)
0 16498
71.5%
2 3
 
< 0.1%
5 23
 
0.1%
11 4
 
< 0.1%
35 34
 
0.1%
41 2
 
< 0.1%
59 3063
 
13.3%
97 1106
 
4.8%
98 106
 
0.5%
122 15
 
0.1%
ValueCountFrequency (%)
225 8
 
< 0.1%
224 4
 
< 0.1%
223 10
 
< 0.1%
222 6
 
< 0.1%
221 8
 
< 0.1%
220 17
 
0.1%
214 19
 
0.1%
213 6
 
< 0.1%
212 100
0.4%
211 8
 
< 0.1%

IDSupportArticle
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.079301075
Minimum0
Maximum6
Zeros22140
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-04-18T18:23:18.249873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49539169
Coefficient of variation (CV)6.2469732
Kurtosis87.865769
Mean0.079301075
Median Absolute Deviation (MAD)0
Skewness8.7389467
Sum1829
Variance0.24541293
MonotonicityNot monotonic
2025-04-18T18:23:18.308894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 22140
96.0%
1 498
 
2.2%
2 286
 
1.2%
5 70
 
0.3%
6 65
 
0.3%
4 4
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 22140
96.0%
1 498
 
2.2%
2 286
 
1.2%
3 1
 
< 0.1%
4 4
 
< 0.1%
5 70
 
0.3%
6 65
 
0.3%
ValueCountFrequency (%)
6 65
 
0.3%
5 70
 
0.3%
4 4
 
< 0.1%
3 1
 
< 0.1%
2 286
 
1.2%
1 498
 
2.2%
0 22140
96.0%
Distinct360
Distinct (%)1.6%
Missing59
Missing (%)0.3%
Memory size1.4 MiB
2025-04-18T18:23:18.503394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length91
Median length4
Mean length5.6357314
Min length4

Characters and Unicode

Total characters129650
Distinct characters60
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique203 ?
Unique (%)0.9%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone
ValueCountFrequency (%)
none 21507
76.8%
polyester 1057
 
3.8%
viscose 737
 
2.6%
elasthanne 502
 
1.8%
100 369
 
1.3%
0 338
 
1.2%
30 295
 
1.1%
coton 288
 
1.0%
polyamide 230
 
0.8%
70 221
 
0.8%
Other values (246) 2452
 
8.8%
2025-04-18T18:23:18.914761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 25361
19.6%
o 24107
18.6%
n 22827
17.6%
N 21568
16.6%
6695
 
5.2%
% 3202
 
2.5%
s 2998
 
2.3%
0 2072
 
1.6%
t 2069
 
1.6%
l 2015
 
1.6%
Other values (50) 16736
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 87070
67.2%
Uppercase Letter 25582
 
19.7%
Space Separator 6695
 
5.2%
Decimal Number 6457
 
5.0%
Other Punctuation 3787
 
2.9%
Dash Punctuation 59
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 21568
84.3%
P 1324
 
5.2%
V 737
 
2.9%
E 648
 
2.5%
C 325
 
1.3%
A 244
 
1.0%
L 139
 
0.5%
S 133
 
0.5%
O 85
 
0.3%
F 72
 
0.3%
Other values (13) 307
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
e 25361
29.1%
o 24107
27.7%
n 22827
26.2%
s 2998
 
3.4%
t 2069
 
2.4%
l 2015
 
2.3%
a 1445
 
1.7%
y 1351
 
1.6%
r 1228
 
1.4%
i 1210
 
1.4%
Other values (12) 2459
 
2.8%
Decimal Number
ValueCountFrequency (%)
0 2072
32.1%
1 763
 
11.8%
5 669
 
10.4%
3 593
 
9.2%
7 515
 
8.0%
2 464
 
7.2%
8 380
 
5.9%
4 349
 
5.4%
6 337
 
5.2%
9 315
 
4.9%
Other Punctuation
ValueCountFrequency (%)
% 3202
84.6%
. 584
 
15.4%
' 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
6695
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 59
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 112652
86.9%
Common 16998
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 25361
22.5%
o 24107
21.4%
n 22827
20.3%
N 21568
19.1%
s 2998
 
2.7%
t 2069
 
1.8%
l 2015
 
1.8%
a 1445
 
1.3%
y 1351
 
1.2%
P 1324
 
1.2%
Other values (35) 7587
 
6.7%
Common
ValueCountFrequency (%)
6695
39.4%
% 3202
18.8%
0 2072
 
12.2%
1 763
 
4.5%
5 669
 
3.9%
3 593
 
3.5%
. 584
 
3.4%
7 515
 
3.0%
2 464
 
2.7%
8 380
 
2.2%
Other values (5) 1061
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129385
99.8%
None 265
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 25361
19.6%
o 24107
18.6%
n 22827
17.6%
N 21568
16.7%
6695
 
5.2%
% 3202
 
2.5%
s 2998
 
2.3%
0 2072
 
1.6%
t 2069
 
1.6%
l 2015
 
1.6%
Other values (47) 16471
12.7%
None
ValueCountFrequency (%)
é 255
96.2%
É 9
 
3.4%
è 1
 
0.4%

IDAr_Looks
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20287027
Minimum0
Maximum9
Zeros22291
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-04-18T18:23:19.041948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1900047
Coefficient of variation (CV)5.8658404
Kurtosis36.409094
Mean0.20287027
Median Absolute Deviation (MAD)0
Skewness6.1133215
Sum4679
Variance1.4161111
MonotonicityNot monotonic
2025-04-18T18:23:19.166378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 22291
96.6%
8 479
 
2.1%
3 116
 
0.5%
2 106
 
0.5%
6 29
 
0.1%
1 22
 
0.1%
4 18
 
0.1%
5 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 22291
96.6%
1 22
 
0.1%
2 106
 
0.5%
3 116
 
0.5%
4 18
 
0.1%
5 2
 
< 0.1%
6 29
 
0.1%
8 479
 
2.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 479
 
2.1%
6 29
 
0.1%
5 2
 
< 0.1%
4 18
 
0.1%
3 116
 
0.5%
2 106
 
0.5%
1 22
 
0.1%
0 22291
96.6%

Matiere
Categorical

Imbalance 

Distinct17
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size1.3 MiB
None
22999 
MAILLE
 
14
COTON
 
7
JERSEY
 
6
Crêpe Marocain
 
6
Other values (12)
 
31

Length

Max length14
Median length4
Mean length4.0078047
Min length2

Characters and Unicode

Total characters92432
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 22999
99.7%
MAILLE 14
 
0.1%
COTON 7
 
< 0.1%
JERSEY 6
 
< 0.1%
Crêpe Marocain 6
 
< 0.1%
Satin 4
 
< 0.1%
DEVEAUX 4
 
< 0.1%
PU 3
 
< 0.1%
Denim 3
 
< 0.1%
Jersey 3
 
< 0.1%
Other values (7) 14
 
0.1%

Length

2025-04-18T18:23:19.286724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 22999
99.7%
maille 14
 
0.1%
coton 11
 
< 0.1%
jersey 9
 
< 0.1%
crêpe 8
 
< 0.1%
marocain 6
 
< 0.1%
satin 6
 
< 0.1%
deveaux 4
 
< 0.1%
denim 4
 
< 0.1%
pu 3
 
< 0.1%
Other values (4) 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 23016
24.9%
N 23015
24.9%
n 23012
24.9%
o 23005
24.9%
E 46
 
< 0.1%
L 32
 
< 0.1%
O 27
 
< 0.1%
C 24
 
< 0.1%
I 22
 
< 0.1%
M 21
 
< 0.1%
Other values (23) 212
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 69114
74.8%
Uppercase Letter 23308
 
25.2%
Space Separator 10
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 23015
98.7%
E 46
 
0.2%
L 32
 
0.1%
O 27
 
0.1%
C 24
 
0.1%
I 22
 
0.1%
M 21
 
0.1%
A 20
 
0.1%
S 20
 
0.1%
T 17
 
0.1%
Other values (9) 64
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
e 23016
33.3%
n 23012
33.3%
o 23005
33.3%
r 17
 
< 0.1%
a 16
 
< 0.1%
i 13
 
< 0.1%
ê 8
 
< 0.1%
p 8
 
< 0.1%
c 6
 
< 0.1%
t 4
 
< 0.1%
Other values (3) 9
 
< 0.1%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 92422
> 99.9%
Common 10
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 23016
24.9%
N 23015
24.9%
n 23012
24.9%
o 23005
24.9%
E 46
 
< 0.1%
L 32
 
< 0.1%
O 27
 
< 0.1%
C 24
 
< 0.1%
I 22
 
< 0.1%
M 21
 
< 0.1%
Other values (22) 202
 
0.2%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92424
> 99.9%
None 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 23016
24.9%
N 23015
24.9%
n 23012
24.9%
o 23005
24.9%
E 46
 
< 0.1%
L 32
 
< 0.1%
O 27
 
< 0.1%
C 24
 
< 0.1%
I 22
 
< 0.1%
M 21
 
< 0.1%
Other values (22) 204
 
0.2%
None
ValueCountFrequency (%)
ê 8
100.0%

Interactions

2025-04-18T18:23:04.199867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:08.667694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:12.811887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-18T18:22:56.042645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-18T18:23:04.303059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-18T18:22:13.206661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:16.636192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:20.217796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:23.710822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-18T18:22:40.804171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:45.509172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:49.461114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:51.656154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:53.684518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:56.247794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:58.462512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:00.150719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:02.271911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:04.556507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-18T18:22:22.503747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:26.448909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:30.698587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:35.038405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:39.335297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:43.789424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:48.195814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:50.918386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:52.904095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:55.646105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:57.852127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:59.645477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:01.720037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:03.863337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:05.952056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:12.126865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:15.756771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:19.404997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:22.763197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:26.657187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:30.911785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:35.298999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:39.632707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:44.035900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:48.424565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:51.019867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:53.000669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:55.758819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:57.945945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:59.730583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:01.795153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:03.943539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:06.064265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:12.363193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:15.967918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:19.568660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:22.993672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:26.893035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:31.402634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:35.518825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:39.829826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:44.256639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:48.630810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:51.125104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:53.128886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:55.850794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:58.038465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:59.817142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:01.882278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:04.032549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:06.152181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:12.587712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:16.129163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:19.731200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:23.213361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:27.150415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:31.695040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:35.693735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:40.027741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:44.494482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:48.887395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:51.298656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:53.310261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:55.949366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:58.162836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:22:59.904570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:01.962215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T18:23:04.119528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-18T18:23:19.391370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AppliqueFodecDateValidationNomenclatureEtatIDArFamilleIDArSousFamilleIDAr_CollectionIDAr_CouleurIDAr_LooksIDArticleIDFournisseurIDGrilleIDPatronnageIDPaysIDSaisonIDSupportArticleIDUsineMatiereModifieParNomenclatureValideNumInternePieceCartonPoidsBrutPoidsNetPrixPrixAchatPrixFacSaisiParTauxTVATypeArticleService
AppliqueFodec1.0000.0120.0710.2670.2690.9090.4770.2660.8770.9470.2871.0000.6250.8690.2920.8860.0711.0000.0100.8041.0000.0100.0100.0130.0130.0130.0000.6290.403
DateValidationNomenclature0.0121.0000.0000.0000.0000.0180.0000.0000.0080.0000.0000.0120.0000.0000.0000.0170.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.000
Etat0.0710.0001.0000.0190.0390.1240.0320.0850.0900.1010.0200.0710.0420.0600.0930.1310.0000.7700.0000.0360.0770.0000.0000.0000.0000.0000.8180.0740.006
IDArFamille0.2670.0000.0191.0000.214-0.1350.074-0.026-0.049-0.1190.3990.2670.2400.058-0.021-0.1030.0460.3250.000-0.1480.165-0.006-0.017-0.104-0.162-0.1840.3640.1320.095
IDArSousFamille0.2690.0000.0390.2141.0000.067-0.0150.0460.0610.0800.2910.269-0.1350.0470.0460.0580.0580.3540.000-0.0120.1750.0160.1030.0480.0400.0260.3880.1100.133
IDAr_Collection0.9090.0180.124-0.1350.0671.000-0.7000.3100.7240.930-0.2060.9090.323-0.4710.3440.8360.1130.4640.0160.8610.5660.076-0.585-0.480-0.208-0.2200.5820.4980.527
IDAr_Couleur0.4770.0000.0320.074-0.015-0.7001.000-0.224-0.522-0.6930.1950.477-0.2850.490-0.238-0.6250.0000.0870.000-0.6980.276-0.0660.4720.4120.2000.2490.0790.1730.144
IDAr_Looks0.2660.0000.085-0.0260.0460.310-0.2241.0000.2550.313-0.0250.2660.035-0.1130.5630.2790.0450.2400.0000.2310.279-0.006-0.209-0.231-0.057-0.1130.3450.4970.256
IDArticle0.8770.0080.090-0.0490.0610.724-0.5220.2551.0000.709-0.1520.8760.147-0.3640.2640.6320.0470.3650.0310.6680.5110.047-0.549-0.530-0.271-0.2360.5930.4370.519
IDFournisseur0.9470.0000.101-0.1190.0800.930-0.6930.3130.7091.000-0.1680.9470.335-0.5050.3210.9070.0570.4560.0000.8850.7080.089-0.583-0.450-0.188-0.2140.4540.4030.399
IDGrille0.2870.0000.0200.3990.291-0.2060.195-0.025-0.152-0.1681.0000.2870.2050.194-0.042-0.1630.0000.2360.000-0.2290.187-0.003-0.029-0.139-0.363-0.3750.2570.1170.066
IDPatronnage1.0000.0120.0710.2670.2690.9090.4770.2660.8760.9470.2871.0000.6250.8690.2920.8860.0710.0000.0100.8051.0000.0100.0100.0130.0130.0130.0000.6280.403
IDPays0.6250.0000.0420.240-0.1350.323-0.2850.0350.1470.3350.2050.6251.000-0.1540.0020.4410.0000.2650.0000.2970.363-0.063-0.305-0.237-0.164-0.1940.3500.2310.141
IDSaison0.8690.0000.0600.0580.047-0.4710.490-0.113-0.364-0.5050.1940.869-0.1541.000-0.119-0.4930.0160.4190.002-0.5990.5060.0610.4580.2610.1560.1540.5110.3230.347
IDSupportArticle0.2920.0000.093-0.0210.0460.344-0.2380.5630.2640.321-0.0420.2920.002-0.1191.0000.2500.2430.2500.0000.2370.2540.013-0.225-0.216-0.098-0.1400.4750.4480.157
IDUsine0.8860.0170.131-0.1030.0580.836-0.6250.2790.6320.907-0.1630.8860.441-0.4930.2501.0000.0860.4850.0200.8390.6570.097-0.523-0.380-0.118-0.1440.5640.4600.418
Matiere0.0710.0000.0000.0460.0580.1130.0000.0450.0470.0570.0000.0710.0000.0160.2430.0861.0000.1330.0000.0510.1950.0000.0000.0000.0000.0000.1770.1260.000
ModifiePar1.0000.0000.7700.3250.3540.4640.0870.2400.3650.4560.2360.0000.2650.4190.2500.4850.1331.0000.0000.2590.4870.0810.0000.0000.0000.0000.5270.6210.583
NomenclatureValide0.0101.0000.0000.0000.0000.0160.0000.0000.0310.0000.0000.0100.0000.0020.0000.0200.0000.0001.0000.0160.0130.0000.0000.0000.0000.0000.0000.0240.000
NumInterne0.8040.0000.036-0.148-0.0120.861-0.6980.2310.6680.885-0.2290.8050.297-0.5990.2370.8390.0510.2590.0161.0000.4730.046-0.607-0.464-0.213-0.2030.2990.3210.346
PieceCarton1.0000.0000.0770.1650.1750.5660.2760.2790.5110.7080.1871.0000.3630.5060.2540.6570.1950.4870.0130.4731.0000.0670.0000.0050.0050.0050.4830.3700.347
PoidsBrut0.0100.0000.000-0.0060.0160.076-0.066-0.0060.0470.089-0.0030.010-0.0630.0610.0130.0970.0000.0810.0000.0460.0671.0000.061-0.044-0.060-0.0520.0840.0140.000
PoidsNet0.0100.0000.000-0.0170.103-0.5850.472-0.209-0.549-0.583-0.0290.010-0.3050.458-0.225-0.5230.0000.0000.000-0.6070.0000.0611.0000.5500.4600.5010.0000.0140.000
Prix0.0130.0000.000-0.1040.048-0.4800.412-0.231-0.530-0.450-0.1390.013-0.2370.261-0.216-0.3800.0000.0000.000-0.4640.005-0.0440.5501.0000.6570.5940.0000.0360.000
PrixAchat0.0130.0000.000-0.1620.040-0.2080.200-0.057-0.271-0.188-0.3630.013-0.1640.156-0.098-0.1180.0000.0000.000-0.2130.005-0.0600.4600.6571.0000.8120.0000.0360.000
PrixFac0.0130.0000.000-0.1840.026-0.2200.249-0.113-0.236-0.214-0.3750.013-0.1940.154-0.140-0.1440.0000.0000.000-0.2030.005-0.0520.5010.5940.8121.0000.0000.0360.000
SaisiPar0.0000.0000.8180.3640.3880.5820.0790.3450.5930.4540.2570.0000.3500.5110.4750.5640.1770.5270.0000.2990.4830.0840.0000.0000.0000.0001.0000.6560.604
TauxTVA0.6290.0200.0740.1320.1100.4980.1730.4970.4370.4030.1170.6280.2310.3230.4480.4600.1260.6210.0240.3210.3700.0140.0140.0360.0360.0360.6561.0000.105
TypeArticleService0.4030.0000.0060.0950.1330.5270.1440.2560.5190.3990.0660.4030.1410.3470.1570.4180.0000.5830.0000.3460.3470.0000.0000.0000.0000.0000.6040.1051.000

Missing values

2025-04-18T18:23:06.394128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-18T18:23:06.940852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-18T18:23:07.344648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDAr_CollectionIDArticleCodeArticleIDAr_CouleurIDArFamilleSaisiParSaisiLeModifieParModifieLeEtatPrixTauxTVAImageCompositionPrixFacPoidsBrutPoidsNetIDArSousFamilleIDGrilleReferenceNomenclatureValideIDSaisonNumInterneIDPatronnageIDFournisseurAppliqueFodecTypeArticleServiceDateValidationNomenclatureArticleLongPrixAchatPieceCartonIDPaysDateMEPIDUsineIDSupportArticleCompositionMatiereIDAr_LooksMatiere
002BHNP282CITY P1 BLACK035superviseur2023-11-02superviseur2024-09-18118.5000.0None96% POLYESTER 4% ELASTANE WOVEN0.000.00.0311142624_24K04145-128910NoneNone18.5002551712024-10-041800None0None
226AENC88LOUISON C1 LOUISON NOIR1192superviseur2023-11-02superviseur2023-11-2017.182-1.0NoneTP:100% PES49.990.00.01911LOUISON C101145-121310NoneNone7.182255172None970None0None
307AENU104ANATALIA ECRU LUREX LIGHT GOLD1204superviseur2023-11-02superviseur2023-10-31113.5000.0None66%Acrylique 18%Polyamide 8%Laine49.990.00.0-12NATALIA00145-1010NoneNone13.50025518None00None0None
408AENU102AANANNI GRIS CHAINE SILVER LUREX-20021294superviseur2023-11-02superviseur2023-11-06113.7000.0None40% ACR 30% PA 30% MOHAIR54.990.00.0-12AENU102A00145-1010NoneNone13.700255-1None00None0None
509AENU102NANNI ROUGE POMPIER LUREX-20451254superviseur2023-11-02superviseur2023-11-06113.7000.0None40% ACR 30% PA 30% MOHAIR54.990.00.0-12AENU10200145-1010NoneNone13.700255-1None00None0None
6010AENU105ANICOLO CREME-71911314superviseur2023-11-02superviseur2023-11-02115.7100.0None50%NY 30%ACR 20%BABY ALPAGA54.990.00.0-12AENU10500145-1010NoneNone15.71025518None00None0None
7011AENU105NICOLO VERT SAPIN1304superviseur2023-11-02superviseur2023-11-02115.7100.0None50%NY 30%ACR 20%BABY ALPAGA54.990.00.0-12AENU105A00145-1010NoneNone15.71025518None00None0None
8012AENU119ANORMA BLEU LUCCIO-21881444superviseur2023-11-02s.hayriye2024-12-17115.7100.0None50%NY 30%ACR 20%BABY ALPAGA54.990.00.0-12AENU119A00145-1010NoneNone12.90025518None00None0None
9013AENU119NORMA NOIR-20591224superviseur2023-11-02superviseur2023-11-02112.8000.0None50%NY 30%ACR 20%BABY ALPAGA54.990.00.0-12AENU11900145-1010NoneNone12.80025518None00None0None
10214YHNC196ADILA C1 GERANIUM121-1superviseur2023-11-02superviseur2023-12-0118.550-1.0NoneNaN59.990.00.01911DILA C103145-121310NoneNone8.550255172None590None0None
IDAr_CollectionIDArticleCodeArticleIDAr_CouleurIDArFamilleSaisiParSaisiLeModifieParModifieLeEtatPrixTauxTVAImageCompositionPrixFacPoidsBrutPoidsNetIDArSousFamilleIDGrilleReferenceNomenclatureValideIDSaisonNumInterneIDPatronnageIDFournisseurAppliqueFodecTypeArticleServiceDateValidationNomenclatureArticleLongPrixAchatPieceCartonIDPaysDateMEPIDUsineIDSupportArticleCompositionMatiereIDAr_LooksMatiere
233001323874DHNXA023SSBEVERLY GANT025UP.Ceren2025-02-26UP.Ceren2025-02-2710.00.2None42%Acrylic,27%Nylon,31%Poly0.00.00.02703DHNXA0230625-12891255NoneBEVERLY GANT CORAIL1.872551732025-03-052065None0None
233011323875DHNXA023SSBEVERLY GANT025UP.Ceren2025-02-26UP.Ceren2025-02-2710.00.2None42%Acrylic,27%Nylon,31%Poly0.00.00.02703DHNXA0230625-12891255NoneBEVERLY GANT KAKI1.872551732025-03-052065None0None
233021323876DHNXA023SSBEVERLY GANT025UP.Ceren2025-02-26UP.Ceren2025-02-2710.00.2None42%Acrylic,27%Nylon,31%Poly0.00.00.02703DHNXA0230625-12891255NoneBEVERLY GANT BLUE DENIM1.872551732025-03-052065None0None
233031123877CENXA018KIT DELOVA MULTICOLORE025UP.Ceren2025-02-27UP.Ceren2025-02-2710.00.0None100%PES10.00.00.03093CENXA0180518-121310NoneKIT DELOVA MULTICOLORE2.252551722025-03-28970None0None
233041123878CENXA019KIT JORDAN MULTICOLORE025UP.Ceren2025-02-27UP.Ceren2025-02-2710.00.0None100%CV10.00.00.03093CENXA0190519-121310NoneKIT JORDAN MULTICOLORE2.252551722025-03-14970None0None
233051123879CENXA020KIT KYOTO MULTICOLORE025UP.Ceren2025-02-27UP.Ceren2025-02-2710.00.0None100%CV10.00.00.03093CENXA0200520-121310NoneKIT KYOTO MULTICOLORE2.252551722025-03-14970None0None
233061123880CENXA021CHOUCHOU TOLOSANE NOIR/BLANC025UP.Ceren2025-02-27NaN2025-02-2710.00.0None100%PES5.00.00.03093CENXA0210521-121310NoneCHOUCHOU TOLOSANE NOIR/BLANC1.252551722025-03-14970None0None
233071123881CENB148FLORIAN SH101UP.Ceren2025-02-27NaNNaT10.00.0NoneNaN0.00.00.01901CENB14805148-121310NoneFLORIAN SH1 LIN RAYÉ LUREX0.00255172None970None0None
233081123882CENT227PENELOPE T1 MIDNIGHT BLUE06b.zuhal2025-02-28b.zuhal2025-02-2810.00.0None100% COTON29.00.00.02162CENT22705227-128910NonePENELOPE T1 MIDNIGHT BLUE6.002551712025-03-151720None0None
233091123883CENT227PENELOPE T1 BLANC06b.zuhal2025-02-28b.zuhal2025-02-2810.00.0None100% COTON29.00.00.02162CENT22705228-128910NonePENELOPE T1 BLANC6.002551712025-03-151720None0None